Overview

Dataset statistics

Number of variables13
Number of observations6497
Missing cells38
Missing cells (%)< 0.1%
Duplicate rows983
Duplicate rows (%)15.1%
Total size in memory660.0 KiB
Average record size in memory104.0 B

Variable types

Categorical1
Numeric12

Alerts

Dataset has 983 (15.1%) duplicate rowsDuplicates
residual sugar is highly correlated with densityHigh correlation
chlorides is highly correlated with type and 1 other fieldsHigh correlation
free sulfur dioxide is highly correlated with total sulfur dioxideHigh correlation
total sulfur dioxide is highly correlated with type and 2 other fieldsHigh correlation
density is highly correlated with fixed acidity and 2 other fieldsHigh correlation
alcohol is highly correlated with densityHigh correlation
type is highly correlated with fixed acidity and 3 other fieldsHigh correlation
fixed acidity is highly correlated with type and 1 other fieldsHigh correlation
volatile acidity is highly correlated with type and 1 other fieldsHigh correlation
sulphates is highly correlated with chloridesHigh correlation
citric acid has 150 (2.3%) zeros Zeros

Reproduction

Analysis started2023-02-01 02:35:31.813194
Analysis finished2023-02-01 02:35:45.728485
Duration13.92 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

type
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.9 KiB
white
4898 
red
1599 

Length

Max length5
Median length5
Mean length4.507772818
Min length3

Characters and Unicode

Total characters29287
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwhite
2nd rowwhite
3rd rowwhite
4th rowwhite
5th rowwhite

Common Values

ValueCountFrequency (%)
white4898
75.4%
red1599
 
24.6%

Length

2023-01-31T18:35:45.766339image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-31T18:35:45.837825image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
white4898
75.4%
red1599
 
24.6%

Most occurring characters

ValueCountFrequency (%)
e6497
22.2%
w4898
16.7%
h4898
16.7%
i4898
16.7%
t4898
16.7%
r1599
 
5.5%
d1599
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter29287
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e6497
22.2%
w4898
16.7%
h4898
16.7%
i4898
16.7%
t4898
16.7%
r1599
 
5.5%
d1599
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Latin29287
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e6497
22.2%
w4898
16.7%
h4898
16.7%
i4898
16.7%
t4898
16.7%
r1599
 
5.5%
d1599
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII29287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e6497
22.2%
w4898
16.7%
h4898
16.7%
i4898
16.7%
t4898
16.7%
r1599
 
5.5%
d1599
 
5.5%

fixed acidity
Real number (ℝ≥0)

HIGH CORRELATION

Distinct106
Distinct (%)1.6%
Missing10
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean7.216579312
Minimum3.8
Maximum15.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-01-31T18:35:45.902790image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile5.7
Q16.4
median7
Q37.7
95-th percentile9.8
Maximum15.9
Range12.1
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.296749857
Coefficient of variation (CV)0.1796903769
Kurtosis5.057726963
Mean7.216579312
Median Absolute Deviation (MAD)0.6
Skewness1.722804531
Sum46813.95
Variance1.68156019
MonotonicityNot monotonic
2023-01-31T18:35:45.991359image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.8354
 
5.4%
6.6326
 
5.0%
6.4305
 
4.7%
7282
 
4.3%
6.9279
 
4.3%
7.2273
 
4.2%
6.7264
 
4.1%
7.1257
 
4.0%
6.5242
 
3.7%
7.4238
 
3.7%
Other values (96)3667
56.4%
ValueCountFrequency (%)
3.81
 
< 0.1%
3.91
 
< 0.1%
4.22
 
< 0.1%
4.43
 
< 0.1%
4.51
 
< 0.1%
4.62
 
< 0.1%
4.76
 
0.1%
4.89
 
0.1%
4.98
 
0.1%
530
0.5%
ValueCountFrequency (%)
15.91
< 0.1%
15.62
< 0.1%
15.52
< 0.1%
152
< 0.1%
14.31
< 0.1%
14.21
< 0.1%
141
< 0.1%
13.81
< 0.1%
13.72
< 0.1%
13.51
< 0.1%

volatile acidity
Real number (ℝ≥0)

HIGH CORRELATION

Distinct187
Distinct (%)2.9%
Missing8
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.3396910156
Minimum0.08
Maximum1.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-01-31T18:35:46.079528image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile0.16
Q10.23
median0.29
Q30.4
95-th percentile0.67
Maximum1.58
Range1.5
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.1646490286
Coefficient of variation (CV)0.4847023357
Kurtosis2.827081281
Mean0.3396910156
Median Absolute Deviation (MAD)0.08
Skewness1.495511586
Sum2204.255
Variance0.02710930263
MonotonicityNot monotonic
2023-01-31T18:35:46.163832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.28286
 
4.4%
0.24265
 
4.1%
0.26255
 
3.9%
0.25238
 
3.7%
0.22235
 
3.6%
0.27232
 
3.6%
0.23221
 
3.4%
0.2217
 
3.3%
0.3214
 
3.3%
0.32205
 
3.2%
Other values (177)4121
63.4%
ValueCountFrequency (%)
0.084
 
0.1%
0.0851
 
< 0.1%
0.091
 
< 0.1%
0.16
 
0.1%
0.1056
 
0.1%
0.1113
 
0.2%
0.1153
 
< 0.1%
0.1237
0.6%
0.1252
 
< 0.1%
0.1344
0.7%
ValueCountFrequency (%)
1.581
< 0.1%
1.332
< 0.1%
1.241
< 0.1%
1.1851
< 0.1%
1.181
< 0.1%
1.131
< 0.1%
1.1151
< 0.1%
1.11
< 0.1%
1.091
< 0.1%
1.071
< 0.1%

citric acid
Real number (ℝ≥0)

ZEROS

Distinct89
Distinct (%)1.4%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.3187218971
Minimum0
Maximum1.66
Zeros150
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-01-31T18:35:46.256021image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05
Q10.25
median0.31
Q30.39
95-th percentile0.56
Maximum1.66
Range1.66
Interquartile range (IQR)0.14

Descriptive statistics

Standard deviation0.1452648005
Coefficient of variation (CV)0.4557728912
Kurtosis2.401582075
Mean0.3187218971
Median Absolute Deviation (MAD)0.07
Skewness0.4730324266
Sum2069.78
Variance0.02110186227
MonotonicityNot monotonic
2023-01-31T18:35:46.338951image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3337
 
5.2%
0.28301
 
4.6%
0.32289
 
4.4%
0.49283
 
4.4%
0.26257
 
4.0%
0.34249
 
3.8%
0.29244
 
3.8%
0.27236
 
3.6%
0.24232
 
3.6%
0.31229
 
3.5%
Other values (79)3837
59.1%
ValueCountFrequency (%)
0150
2.3%
0.0140
 
0.6%
0.0256
 
0.9%
0.0332
 
0.5%
0.0441
 
0.6%
0.0525
 
0.4%
0.0630
 
0.5%
0.0733
 
0.5%
0.0837
 
0.6%
0.0942
 
0.6%
ValueCountFrequency (%)
1.661
 
< 0.1%
1.231
 
< 0.1%
16
0.1%
0.991
 
< 0.1%
0.912
 
< 0.1%
0.881
 
< 0.1%
0.861
 
< 0.1%
0.822
 
< 0.1%
0.812
 
< 0.1%
0.82
 
< 0.1%

residual sugar
Real number (ℝ≥0)

HIGH CORRELATION

Distinct316
Distinct (%)4.9%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.444326405
Minimum0.6
Maximum65.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-01-31T18:35:46.426351image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile1.2
Q11.8
median3
Q38.1
95-th percentile15
Maximum65.8
Range65.2
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.758124743
Coefficient of variation (CV)0.8739602274
Kurtosis4.358134414
Mean5.444326405
Median Absolute Deviation (MAD)1.7
Skewness1.434999839
Sum35360.9
Variance22.63975107
MonotonicityNot monotonic
2023-01-31T18:35:46.509632image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2235
 
3.6%
1.8228
 
3.5%
1.6223
 
3.4%
1.4219
 
3.4%
1.2195
 
3.0%
2.2187
 
2.9%
2.1179
 
2.8%
1.9176
 
2.7%
1.7175
 
2.7%
1.5171
 
2.6%
Other values (306)4507
69.4%
ValueCountFrequency (%)
0.62
 
< 0.1%
0.77
 
0.1%
0.825
 
0.4%
0.941
 
0.6%
0.954
 
0.1%
193
1.4%
1.051
 
< 0.1%
1.1146
2.2%
1.153
 
< 0.1%
1.2195
3.0%
ValueCountFrequency (%)
65.81
< 0.1%
31.62
< 0.1%
26.052
< 0.1%
23.51
< 0.1%
22.61
< 0.1%
222
< 0.1%
20.82
< 0.1%
20.72
< 0.1%
20.41
< 0.1%
20.31
< 0.1%

chlorides
Real number (ℝ≥0)

HIGH CORRELATION

Distinct214
Distinct (%)3.3%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.05604157044
Minimum0.009
Maximum0.611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-01-31T18:35:46.597577image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.009
5-th percentile0.028
Q10.038
median0.047
Q30.065
95-th percentile0.102
Maximum0.611
Range0.602
Interquartile range (IQR)0.027

Descriptive statistics

Standard deviation0.03503602523
Coefficient of variation (CV)0.6251792188
Kurtosis50.89487358
Mean0.05604157044
Median Absolute Deviation (MAD)0.011
Skewness5.399848763
Sum363.99
Variance0.001227523064
MonotonicityNot monotonic
2023-01-31T18:35:46.682037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.044206
 
3.2%
0.036200
 
3.1%
0.042187
 
2.9%
0.046185
 
2.8%
0.05182
 
2.8%
0.04182
 
2.8%
0.048182
 
2.8%
0.047175
 
2.7%
0.045174
 
2.7%
0.034169
 
2.6%
Other values (204)4653
71.6%
ValueCountFrequency (%)
0.0091
 
< 0.1%
0.0123
 
< 0.1%
0.0131
 
< 0.1%
0.0144
 
0.1%
0.0154
 
0.1%
0.0165
 
0.1%
0.0175
 
0.1%
0.01810
0.2%
0.0199
0.1%
0.0216
0.2%
ValueCountFrequency (%)
0.6111
 
< 0.1%
0.611
 
< 0.1%
0.4671
 
< 0.1%
0.4641
 
< 0.1%
0.4221
 
< 0.1%
0.4153
< 0.1%
0.4142
< 0.1%
0.4131
 
< 0.1%
0.4031
 
< 0.1%
0.4011
 
< 0.1%

free sulfur dioxide
Real number (ℝ≥0)

HIGH CORRELATION

Distinct135
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.52531938
Minimum1
Maximum289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-01-31T18:35:46.771485image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q117
median29
Q341
95-th percentile61
Maximum289
Range288
Interquartile range (IQR)24

Descriptive statistics

Standard deviation17.74939977
Coefficient of variation (CV)0.5814648342
Kurtosis7.906238067
Mean30.52531938
Median Absolute Deviation (MAD)12
Skewness1.220066074
Sum198323
Variance315.0411923
MonotonicityNot monotonic
2023-01-31T18:35:46.855321image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29183
 
2.8%
6170
 
2.6%
26161
 
2.5%
15157
 
2.4%
24152
 
2.3%
31152
 
2.3%
17149
 
2.3%
34146
 
2.2%
35144
 
2.2%
23142
 
2.2%
Other values (125)4941
76.1%
ValueCountFrequency (%)
13
 
< 0.1%
22
 
< 0.1%
359
 
0.9%
452
 
0.8%
5129
2.0%
5.51
 
< 0.1%
6170
2.6%
796
1.5%
891
1.4%
991
1.4%
ValueCountFrequency (%)
2891
< 0.1%
146.51
< 0.1%
138.51
< 0.1%
1311
< 0.1%
1281
< 0.1%
1241
< 0.1%
122.51
< 0.1%
118.51
< 0.1%
1121
< 0.1%
1101
< 0.1%

total sulfur dioxide
Real number (ℝ≥0)

HIGH CORRELATION

Distinct276
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.7445744
Minimum6
Maximum440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-01-31T18:35:46.949352image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile19
Q177
median118
Q3156
95-th percentile206
Maximum440
Range434
Interquartile range (IQR)79

Descriptive statistics

Standard deviation56.52185452
Coefficient of variation (CV)0.488332648
Kurtosis-0.3716636549
Mean115.7445744
Median Absolute Deviation (MAD)39
Skewness-0.001177478234
Sum751992.5
Variance3194.720039
MonotonicityNot monotonic
2023-01-31T18:35:47.030514image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11172
 
1.1%
11365
 
1.0%
11757
 
0.9%
12257
 
0.9%
12456
 
0.9%
12856
 
0.9%
11456
 
0.9%
9856
 
0.9%
11855
 
0.8%
11954
 
0.8%
Other values (266)5913
91.0%
ValueCountFrequency (%)
63
 
< 0.1%
74
 
0.1%
814
 
0.2%
915
0.2%
1028
0.4%
1126
0.4%
1229
0.4%
1328
0.4%
1433
0.5%
1535
0.5%
ValueCountFrequency (%)
4401
< 0.1%
366.51
< 0.1%
3441
< 0.1%
3131
< 0.1%
307.51
< 0.1%
3031
< 0.1%
2941
< 0.1%
2891
< 0.1%
2821
< 0.1%
2781
< 0.1%

density
Real number (ℝ≥0)

HIGH CORRELATION

Distinct998
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9946966338
Minimum0.98711
Maximum1.03898
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-01-31T18:35:47.113486image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.98711
5-th percentile0.9899
Q10.99234
median0.99489
Q30.99699
95-th percentile0.999392
Maximum1.03898
Range0.05187
Interquartile range (IQR)0.00465

Descriptive statistics

Standard deviation0.002998673004
Coefficient of variation (CV)0.003014660854
Kurtosis6.606066991
Mean0.9946966338
Median Absolute Deviation (MAD)0.00231
Skewness0.5036017301
Sum6462.54403
Variance8.992039783 × 10-6
MonotonicityNot monotonic
2023-01-31T18:35:47.357859image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.997269
 
1.1%
0.997669
 
1.1%
0.99264
 
1.0%
0.99864
 
1.0%
0.992863
 
1.0%
0.998661
 
0.9%
0.996659
 
0.9%
0.996259
 
0.9%
0.995655
 
0.8%
0.996855
 
0.8%
Other values (988)5879
90.5%
ValueCountFrequency (%)
0.987111
< 0.1%
0.987131
< 0.1%
0.987221
< 0.1%
0.98741
< 0.1%
0.987422
< 0.1%
0.987462
< 0.1%
0.987581
< 0.1%
0.987741
< 0.1%
0.987791
< 0.1%
0.987942
< 0.1%
ValueCountFrequency (%)
1.038981
 
< 0.1%
1.01032
< 0.1%
1.003692
< 0.1%
1.00321
 
< 0.1%
1.003153
< 0.1%
1.002952
< 0.1%
1.002891
 
< 0.1%
1.00262
< 0.1%
1.002422
< 0.1%
1.002411
 
< 0.1%

pH
Real number (ℝ≥0)

Distinct108
Distinct (%)1.7%
Missing9
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean3.218395499
Minimum2.72
Maximum4.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-01-31T18:35:47.447694image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2.72
5-th percentile2.97
Q13.11
median3.21
Q33.32
95-th percentile3.5
Maximum4.01
Range1.29
Interquartile range (IQR)0.21

Descriptive statistics

Standard deviation0.1607483066
Coefficient of variation (CV)0.04994672239
Kurtosis0.370068107
Mean3.218395499
Median Absolute Deviation (MAD)0.11
Skewness0.3869659326
Sum20880.95
Variance0.02584001806
MonotonicityNot monotonic
2023-01-31T18:35:47.538528image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.16200
 
3.1%
3.14193
 
3.0%
3.22185
 
2.8%
3.2176
 
2.7%
3.15170
 
2.6%
3.19170
 
2.6%
3.18168
 
2.6%
3.24160
 
2.5%
3.12154
 
2.4%
3.1154
 
2.4%
Other values (98)4758
73.2%
ValueCountFrequency (%)
2.721
 
< 0.1%
2.742
 
< 0.1%
2.771
 
< 0.1%
2.793
 
< 0.1%
2.83
 
< 0.1%
2.821
 
< 0.1%
2.834
 
0.1%
2.841
 
< 0.1%
2.859
0.1%
2.8610
0.2%
ValueCountFrequency (%)
4.012
< 0.1%
3.92
< 0.1%
3.851
< 0.1%
3.821
< 0.1%
3.811
< 0.1%
3.82
< 0.1%
3.791
< 0.1%
3.782
< 0.1%
3.772
< 0.1%
3.762
< 0.1%

sulphates
Real number (ℝ≥0)

HIGH CORRELATION

Distinct111
Distinct (%)1.7%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.5312151548
Minimum0.22
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-01-31T18:35:47.628239image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.22
5-th percentile0.35
Q10.43
median0.51
Q30.6
95-th percentile0.79
Maximum2
Range1.78
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.1488141213
Coefficient of variation (CV)0.2801390735
Kurtosis8.659892183
Mean0.5312151548
Median Absolute Deviation (MAD)0.08
Skewness1.798467034
Sum3449.18
Variance0.0221456427
MonotonicityNot monotonic
2023-01-31T18:35:47.713213image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5275
 
4.2%
0.46243
 
3.7%
0.54234
 
3.6%
0.44232
 
3.6%
0.38214
 
3.3%
0.48208
 
3.2%
0.52203
 
3.1%
0.49197
 
3.0%
0.47191
 
2.9%
0.45190
 
2.9%
Other values (101)4306
66.3%
ValueCountFrequency (%)
0.221
 
< 0.1%
0.231
 
< 0.1%
0.254
 
0.1%
0.264
 
0.1%
0.2713
 
0.2%
0.2813
 
0.2%
0.2916
 
0.2%
0.331
0.5%
0.3135
0.5%
0.3254
0.8%
ValueCountFrequency (%)
21
 
< 0.1%
1.981
 
< 0.1%
1.952
< 0.1%
1.621
 
< 0.1%
1.611
 
< 0.1%
1.591
 
< 0.1%
1.561
 
< 0.1%
1.363
< 0.1%
1.341
 
< 0.1%
1.331
 
< 0.1%

alcohol
Real number (ℝ≥0)

HIGH CORRELATION

Distinct111
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.49180083
Minimum8
Maximum14.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-01-31T18:35:47.803746image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile9
Q19.5
median10.3
Q311.3
95-th percentile12.7
Maximum14.9
Range6.9
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.192711749
Coefficient of variation (CV)0.1136803651
Kurtosis-0.5316873829
Mean10.49180083
Median Absolute Deviation (MAD)0.9
Skewness0.5657177291
Sum68165.23
Variance1.422561316
MonotonicityNot monotonic
2023-01-31T18:35:47.888595image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.5367
 
5.6%
9.4332
 
5.1%
9.2271
 
4.2%
10229
 
3.5%
10.5227
 
3.5%
11217
 
3.3%
9215
 
3.3%
9.8214
 
3.3%
10.4194
 
3.0%
9.3193
 
3.0%
Other values (101)4038
62.2%
ValueCountFrequency (%)
82
 
< 0.1%
8.45
 
0.1%
8.510
 
0.2%
8.623
 
0.4%
8.780
 
1.2%
8.8109
1.7%
8.995
1.5%
9215
3.3%
9.051
 
< 0.1%
9.1167
2.6%
ValueCountFrequency (%)
14.91
 
< 0.1%
14.21
 
< 0.1%
14.051
 
< 0.1%
1412
0.2%
13.93
 
< 0.1%
13.82
 
< 0.1%
13.77
0.1%
13.613
0.2%
13.566666671
 
< 0.1%
13.551
 
< 0.1%

quality
Real number (ℝ≥0)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.818377713
Minimum3
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-01-31T18:35:47.962636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum9
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8732552715
Coefficient of variation (CV)0.1500856965
Kurtosis0.2323222693
Mean5.818377713
Median Absolute Deviation (MAD)1
Skewness0.1896226934
Sum37802
Variance0.7625747693
MonotonicityNot monotonic
2023-01-31T18:35:48.014352image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
62836
43.7%
52138
32.9%
71079
 
16.6%
4216
 
3.3%
8193
 
3.0%
330
 
0.5%
95
 
0.1%
ValueCountFrequency (%)
330
 
0.5%
4216
 
3.3%
52138
32.9%
62836
43.7%
71079
 
16.6%
8193
 
3.0%
95
 
0.1%
ValueCountFrequency (%)
95
 
0.1%
8193
 
3.0%
71079
 
16.6%
62836
43.7%
52138
32.9%
4216
 
3.3%
330
 
0.5%

Interactions

2023-01-31T18:35:44.290160image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:34.175086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:35.086290image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:36.113997image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:36.964878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:37.973082image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:38.837342image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:39.708013image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:40.697221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:41.542267image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:42.390096image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:43.419913image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:44.361073image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:34.275354image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:35.160535image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:36.183756image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:37.035475image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:38.044467image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:38.909979image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:39.776876image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:40.768163image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:41.612279image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:42.462788image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:43.492041image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:44.434701image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:34.351128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:35.237264image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:36.256525image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:37.108806image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:38.119124image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:38.987912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:39.849565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:40.841886image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:41.685792image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:42.538288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:43.566720image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:44.505486image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:34.425583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:35.455466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:36.326280image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:37.179405image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:38.191191image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:39.061543image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:39.921639image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:40.911897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:41.756137image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:42.610947image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:43.638916image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:44.580989image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:34.499381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:35.529289image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:36.395373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:37.249512image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:38.262470image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:39.133710image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:39.991009image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:40.982153image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:41.826549image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:42.683589image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:43.710838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:44.652230image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:34.573203image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:35.603059image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:36.465947image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:37.320579image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:38.334046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:39.206392image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:40.061074image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:41.052413image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:41.897825image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:42.908089image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:43.782205image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:44.723192image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:34.646897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:35.676833image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:36.535535image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:37.390418image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:38.405595image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:39.278147image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:40.129777image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:41.123172image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:41.969075image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:42.981564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:43.853740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:44.789766image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:34.716383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:35.746786image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:36.601949image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:37.457998image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:38.473725image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:39.345749image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:40.194950image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:41.189501image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:42.035352image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:43.050387image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:43.924186image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:44.859090image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:34.789580image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:35.819228image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:36.672345image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:37.527147image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:38.544080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:39.416403image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:40.412879image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:41.258826image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:42.104394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:43.122784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:43.995150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:44.931143image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:34.863332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:35.892922image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:36.743980image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:37.597108image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:38.615629image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:39.488313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:40.483039image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:41.328260image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:42.174138image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:43.195664image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:44.068521image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:45.004495image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:34.939795image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:35.969223image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:36.818811image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:37.670032image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:38.690476image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:39.562577image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:40.555526image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:41.400368image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:42.247354image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:43.271724image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:44.142882image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:45.076755image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:35.013921image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:36.042648image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:36.892896image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:37.900391image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:38.764918image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:39.636326image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:40.627395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:41.472629image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:42.320353image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:43.347040image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-31T18:35:44.216820image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-01-31T18:35:48.079145image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-01-31T18:35:48.184860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-01-31T18:35:48.291027image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-01-31T18:35:48.399863image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-01-31T18:35:45.340700image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-31T18:35:45.490146image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-01-31T18:35:45.601021image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2023-01-31T18:35:45.668901image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

typefixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
0white7.00.270.3620.70.04545.0170.01.00103.000.458.86
1white6.30.300.341.60.04914.0132.00.99403.300.499.56
2white8.10.280.406.90.05030.097.00.99513.260.4410.16
3white7.20.230.328.50.05847.0186.00.99563.190.409.96
4white7.20.230.328.50.05847.0186.00.99563.190.409.96
5white8.10.280.406.90.05030.097.00.99513.260.4410.16
6white6.20.320.167.00.04530.0136.00.99493.180.479.66
7white7.00.270.3620.70.04545.0170.01.00103.000.458.86
8white6.30.300.341.60.04914.0132.00.99403.300.499.56
9white8.10.220.431.50.04428.0129.00.99383.220.4511.06

Last rows

typefixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
6487red6.60.7250.207.80.07329.079.00.997703.290.549.25
6488red6.30.5500.151.80.07726.035.00.993143.320.8211.66
6489red5.40.7400.091.70.08916.026.00.994023.670.5611.66
6490red6.30.5100.132.30.07629.040.00.995743.420.7511.06
6491red6.80.6200.081.90.06828.038.00.996513.420.829.56
6492red6.20.6000.082.00.09032.044.00.994903.450.5810.55
6493red5.90.5500.102.20.06239.051.00.995123.52NaN11.26
6494red6.30.5100.132.30.07629.040.00.995743.420.7511.06
6495red5.90.6450.122.00.07532.044.00.995473.570.7110.25
6496red6.00.3100.473.60.06718.042.00.995493.390.6611.06

Duplicate rows

Most frequently occurring

typefixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality# duplicates
640white7.00.150.2814.70.05129.0149.00.997922.960.399.078
772white7.30.190.2713.90.05745.0155.00.998072.940.418.888
553white6.80.180.3012.80.06219.0171.00.998083.000.529.077
803white7.40.160.3013.70.05633.0168.00.998252.900.448.777
802white7.40.160.2715.50.05025.0135.00.998402.900.438.776
806white7.40.190.3012.80.05348.5229.00.998603.140.499.176
807white7.40.190.3114.50.04539.0193.00.998603.100.509.266
854white7.60.200.3014.20.05653.0212.50.999003.140.468.986
248white5.70.220.2016.00.04441.0113.00.998623.220.468.965
330white6.20.230.3617.20.03937.0130.00.999463.230.438.865